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Runoff conditions in the Fuping Basin under an ensemble of climate change scenarios

ROMAINE Ingabire CAO Bo CAO Jiansheng ZHANG Xiaolong LIU Xia SHEN Yanjun

ROMAINEIngabire, 曹博, 曹建生, 张晓龙, 刘夏, 沈彦军. 未来气候变化情景下阜平流域径流变化分析[J]. 中国生态农业学报(中英文), 2022, 30(5): 851-863. doi: 10.12357/cjea.20210725
引用本文: ROMAINEIngabire, 曹博, 曹建生, 张晓龙, 刘夏, 沈彦军. 未来气候变化情景下阜平流域径流变化分析[J]. 中国生态农业学报(中英文), 2022, 30(5): 851-863. doi: 10.12357/cjea.20210725
ROMAINE I, CAO B, CAO J S, ZHANG X L, LIU X, SHEN Y J. Runoff conditions in the Fuping Basin under an ensemble of climate change scenarios[J]. Chinese Journal of Eco-Agriculture, 2022, 30(5): 851−863 doi: 10.12357/cjea.20210725
Citation: ROMAINE I, CAO B, CAO J S, ZHANG X L, LIU X, SHEN Y J. Runoff conditions in the Fuping Basin under an ensemble of climate change scenarios[J]. Chinese Journal of Eco-Agriculture, 2022, 30(5): 851−863 doi: 10.12357/cjea.20210725

未来气候变化情景下阜平流域径流变化分析

doi: 10.12357/cjea.20210725
详细信息
  • 中图分类号: P333; P467

Runoff conditions in the Fuping Basin under an ensemble of climate change scenarios

Funds: This research was supported by the Foundation for Innovative Research Groups of the Natural Science Foundation of Hebei Province (D2021503001) and the National Natural Science Foundation of China (41877170, 41807177).
More Information
  • 摘要: 径流变化对于水资源管理至关重要, 然而, 未来气候变化对阜平流域径流的影响仍未知。本文基于实测数据及4个区域气候模式(RCMs)的集合数据, 使用MIKE11-NAM模型模拟了阜平流域(大清河流域上游的子流域)当前(2008—2017年)及在SSP1-2.6、SSP2-4.5和SSP5-8.5 3种情景下的未来(2025—2054年)径流变化情况。结果表明, MIKE11-NAM模型在日径流模拟中表现良好, R²和NSE在校准期分别为0.82和0.81, 在验证期分别为0.87和0.87。偏差校正后, 观测数据和RCM数据间的相关性提高。与基准期(1985—2014年)相比, 未来的降水量和气温均呈现增加趋势。在SSP5-8.5和SSP2-4.5情景下, 年均温和降水量将分别增加2.45 ℃和124 mm。预计夏季降水量增加幅度较大, 特别是在7—8月; 而各季节的气温将上升, 其中冬季气温上升幅度最大。在SSP2-4.5情景下, 预计年径流量将增加3.5 mm; 而在SSP1-2.6和SSP5-8.5情景下, 预计年径流量将分别减少12.0 mm和11.0 mm。季节尺度上, 在SSP1-2.6、SSP2-4.5和SSP5-8.5情景下, 未来春季径流量将分别减少2.3 mm、1.2 mm和1.9 mm, 夏季径流量将分别减少9.0 mm、7.1 mm和12.9 mm。研究结果可为该地区水资源综合管理和规划提供科学参考。
  • Figure  1.  Location and elevation of, and spatial distribution of meteorological and hydrological stations in the Fuping Basin

    Figure  2.  MIKE11-NAM structure (DHI, 2009)

    QOF: overland flow; QIF: interflow; GWPUMP: groundwater pumping; GWL: maximum groundwater depth; CAFLUX: capillar flux; Sy: specific yield; BF: baseflow.

    Figure  3.  Trend analysis of annual precipitation, temperature and runoff in the Fuping Basin from 1980 to 2017

    Figure  4.  Observed vs. simulated runoff for both calibration (a, 2009−2011) and validation (b, 2012−2017) periods of the Fuping Basin

    Figure  5.  Future (2025−2054) monthly precipitation projection based on multi-model ensemble of four regional climate models (MME) compared to the baseline (1985−2014) precipitation

    Figure  6.  Future (2025−2054) monthly temperature projection based on multi-model ensemble of four regional climate models (MME) compared to the baseline (1985−2014) temperature

    Figure  7.  Projected future monthly runoff (2025−2054) in the Fuping Basin under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios compare to baseline (1985−2014)

    Table  1.   Parameters used in the MIKE11-NAM model during simulation

    ParameterDescriptionValue rangeFinal value
    Umax (mm)Maximum water content in the surface storage10−2012.3
    Lmax (mm)Maximum water content in root zone storage50−30050.5
    CQOFOverland flow runoff coefficient0−10.894
    CKIF (h)Time constant for routing interflow200−1000449.4
    CK1,2 (h)Time constant for routing overland flow3−4840.5
    TOFRoot zone threshold value for overland flow0−0.990.685
    TIFRoot zone threshold value for interflow0−0.990.0121
    TGRoot zone threshold value for groundwater recharge0−0.990.154
    CKBF (h)Time constant for routing base flow1−50002761
    下载: 导出CSV

    Table  2.   Trends of temperature, precipitation and runoff from 1980 to 2017 in the Fuping Basin based on the Mann-Kendall test

    TimeTemperaturePrecipitationRunoff
    ℃∙decade−1P-valuemm∙decade−1P-valuemm∙decade−1P-value
    Annual0.4<0.0018.00.33−2.20.36
    Spring0.6<0.001−3.00.300.40.29
    Summer0.3<0.05−2.40.38−3.00.19
    Autumn0.3<0.0517.5<0.0010.80.34
    Winter0.7<0.0011.00.09−0.30.32
    下载: 导出CSV

    Table  3.   Bias correction performance of four regional climate models for monthly precipitation and temperature estimation compared to the observed data

    VariableModelBefore correctionAfter correction
    R2RMSE
    R2RMSE
    TemperatureACCESS_ESM1_50.972.18 ℃0.971.83
    Can_ESM50.954.30 ℃0.952.40
    EC_EARTH30.952.69 ℃0.962.27
    MPI_ESM1_2HR0.962.17 ℃0.972.05
    PrecipitationACCESS_ESM1_50.4350.71 mm0.4349.00
    Can_ESM50.3551.45 mm0.4549.73
    EC_EARTH30.4650.15 mm0.4748.62
    MPI_ESM1_2HR0.4150.51 mm0.4749.85
    下载: 导出CSV

    Table  4.   Precipitation and temperature change comparison between historical observed data (1985−2014) and future data (2025−2054) based on four regional climate models (RCM) and multimodel ensemble under 3 scenarios

    VariableScenarioHistorical
    (1985−2014)
    RCM model (2025−2054)Multi-model
    ensemble (MME)
    ACCESS-ESMI-5Can-ESM5EC-Earth3MPI-ESMI-2HR
    Temperature (℃)SSP1-2.67.8+1.96+2.06+2.54+0.93+1.87
    SSP2-4.5+1.97+2.43+2.44+0.83+1.92
    SSP5-8.5+2.52+2.97+2.92+1.37+2.45
    Precipitation (mm)SSP1-2.6670+72+142+104+70 +97
    SSP2-4.5+99+187+121+89+124
    SSP5-8.5+23+194+129+51 +99
      “+” means the increase.
    下载: 导出CSV
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  • 收稿日期:  2021-10-29
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